ReviewNature methods2024
Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.
Review in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- The evaluation crisis in artificial intelligence-driven computational biology: Pitfalls and solutions.Quantitative biology (Beijing, China) · 2026Article
- Using Brain Organoids to Explore Human Neurobiology.Cellular and molecular neurobiology · 2026Review
- Machine learning reveals sequence and genomic context features underlyingbioRxiv : the preprint server for biology · 2026Article
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.Cell systems · 2026Article
- Article
- A Comprehensive Comparative Analysis of Sequence-Based Deep Learning Models for Single-Cell Genomics.Cell proliferation · 2026Article
- scENature communications · 2026Article
- Integrating pathological morphologies and molecular profiles for clinical biomarker discovery with unified multimodal embedding.Clinical and translational medicine · 2026Article
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi-Omics Using Multimodal Mixture of Disentangled Experts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- INBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- Article
- scMarkerGene: an interpretable neural network framework for cell-type-specific marker gene discovery.Briefings in bioinformatics · 2026Article
- Systematically decoding pathological morphologies and molecular profiles with unified multimodal embedding.Nature methods · 2026Article
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks.Cell genomics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However, guidelines for using IML in computational biology are generally underdeveloped. We provide an overview of IML methods and evaluation techniques and discuss common pitfalls encountered when applying IML methods to computational biology problems. We also highlight open questions, especially in the era of large language models, and call for collaboration between IML and computational biology researchers.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.